Overview

Dataset statistics

Number of variables22
Number of observations8101
Missing cells2673
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory176.0 B

Variable types

Numeric16
Categorical6

Alerts

Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Card_Category is highly imbalanced (79.4%)Imbalance
Education_Level has 1205 (14.9%) missing valuesMissing
Marital_Status has 579 (7.1%) missing valuesMissing
Income_Category has 889 (11.0%) missing valuesMissing
train_idx is uniformly distributedUniform
train_idx has unique valuesUnique
CLIENTNUM has unique valuesUnique
Dependent_count has 725 (8.9%) zerosZeros
Contacts_Count_12_mon has 312 (3.9%) zerosZeros
Total_Revolving_Bal has 1986 (24.5%) zerosZeros
Avg_Utilization_Ratio has 1986 (24.5%) zerosZeros

Reproduction

Analysis started2023-10-06 13:34:41.680544
Analysis finished2023-10-06 13:35:18.072237
Duration36.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

train_idx
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4050
Minimum0
Maximum8100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:18.164421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile405
Q12025
median4050
Q36075
95-th percentile7695
Maximum8100
Range8100
Interquartile range (IQR)4050

Descriptive statistics

Standard deviation2338.7016
Coefficient of variation (CV)0.57745718
Kurtosis-1.2
Mean4050
Median Absolute Deviation (MAD)2025
Skewness0
Sum32809050
Variance5469525.2
MonotonicityStrictly increasing
2023-10-06T09:35:18.332528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
5396 1
 
< 0.1%
5409 1
 
< 0.1%
5408 1
 
< 0.1%
5407 1
 
< 0.1%
5406 1
 
< 0.1%
5405 1
 
< 0.1%
5404 1
 
< 0.1%
5403 1
 
< 0.1%
5402 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
8100 1
< 0.1%
8099 1
< 0.1%
8098 1
< 0.1%
8097 1
< 0.1%
8096 1
< 0.1%
8095 1
< 0.1%
8094 1
< 0.1%
8093 1
< 0.1%
8092 1
< 0.1%
8091 1
< 0.1%

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct8101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3913295 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:18.495664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0910126 × 108
Q17.1305338 × 108
median7.1788601 × 108
Q37.7284638 × 108
95-th percentile8.1397128 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)59793000

Descriptive statistics

Standard deviation36919116
Coefficient of variation (CV)0.049949222
Kurtosis-0.60777433
Mean7.3913295 × 108
Median Absolute Deviation (MAD)6292200
Skewness1.0005322
Sum5.987716 × 1012
Variance1.3630211 × 1015
MonotonicityNot monotonic
2023-10-06T09:35:18.655206image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713071383 1
 
< 0.1%
759947433 1
 
< 0.1%
709841433 1
 
< 0.1%
789483333 1
 
< 0.1%
785328408 1
 
< 0.1%
805534608 1
 
< 0.1%
709005633 1
 
< 0.1%
718246458 1
 
< 0.1%
789917208 1
 
< 0.1%
778377183 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
708104658 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%
827984658 1
< 0.1%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.306382
Minimum26
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:18.814608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum70
Range44
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0225266
Coefficient of variation (CV)0.17324883
Kurtosis-0.31128146
Mean46.306382
Median Absolute Deviation (MAD)6
Skewness-0.043812536
Sum375128
Variance64.360933
MonotonicityNot monotonic
2023-10-06T09:35:18.955200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
46 397
 
4.9%
49 395
 
4.9%
45 390
 
4.8%
44 386
 
4.8%
47 378
 
4.7%
48 372
 
4.6%
43 371
 
4.6%
50 367
 
4.5%
42 326
 
4.0%
51 325
 
4.0%
Other values (34) 4394
54.2%
ValueCountFrequency (%)
26 63
0.8%
27 22
 
0.3%
28 24
 
0.3%
29 50
 
0.6%
30 53
 
0.7%
31 78
1.0%
32 90
1.1%
33 98
1.2%
34 126
1.6%
35 143
1.8%
ValueCountFrequency (%)
70 1
 
< 0.1%
68 2
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
65 77
1.0%
64 32
 
0.4%
63 58
0.7%
62 70
0.9%
61 73
0.9%
60 99
1.2%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
F
4279 
M
3822 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Length

2023-10-06T09:35:19.077194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:19.205163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
f 4279
52.8%
m 3822
47.2%

Most occurring characters

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8101
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3347735
Minimum0
Maximum5
Zeros725
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:19.295686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2895637
Coefficient of variation (CV)0.55232927
Kurtosis-0.65676736
Mean2.3347735
Median Absolute Deviation (MAD)1
Skewness-0.020167501
Sum18914
Variance1.6629746
MonotonicityNot monotonic
2023-10-06T09:35:19.398165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2222
27.4%
2 2150
26.5%
1 1465
18.1%
4 1212
15.0%
0 725
 
8.9%
5 327
 
4.0%
ValueCountFrequency (%)
0 725
 
8.9%
1 1465
18.1%
2 2150
26.5%
3 2222
27.4%
4 1212
15.0%
5 327
 
4.0%
ValueCountFrequency (%)
5 327
 
4.0%
4 1212
15.0%
3 2222
27.4%
2 2150
26.5%
1 1465
18.1%
0 725
 
8.9%

Education_Level
Categorical

MISSING 

Distinct6
Distinct (%)0.1%
Missing1205
Missing (%)14.9%
Memory size63.4 KiB
Graduate
2528 
High School
1619 
Uneducated
1171 
College
816 
Post-Graduate
407 

Length

Max length13
Median length11
Mean length9.2721868
Min length7

Characters and Unicode

Total characters63941
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowHigh School
4th rowDoctorate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 2528
31.2%
High School 1619
20.0%
Uneducated 1171
14.5%
College 816
 
10.1%
Post-Graduate 407
 
5.0%
Doctorate 355
 
4.4%
(Missing) 1205
14.9%

Length

2023-10-06T09:35:19.516862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:19.659829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2528
29.7%
high 1619
19.0%
school 1619
19.0%
uneducated 1171
13.8%
college 816
 
9.6%
post-graduate 407
 
4.8%
doctorate 355
 
4.2%

Most occurring characters

ValueCountFrequency (%)
a 7396
11.6%
e 7264
11.4%
d 5277
 
8.3%
t 5223
 
8.2%
o 5171
 
8.1%
u 4106
 
6.4%
r 3290
 
5.1%
l 3251
 
5.1%
h 3238
 
5.1%
c 3145
 
4.9%
Other values (13) 16580
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52993
82.9%
Uppercase Letter 8922
 
14.0%
Space Separator 1619
 
2.5%
Dash Punctuation 407
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7396
14.0%
e 7264
13.7%
d 5277
10.0%
t 5223
9.9%
o 5171
9.8%
u 4106
7.7%
r 3290
6.2%
l 3251
6.1%
h 3238
6.1%
c 3145
5.9%
Other values (4) 5632
10.6%
Uppercase Letter
ValueCountFrequency (%)
G 2935
32.9%
S 1619
18.1%
H 1619
18.1%
U 1171
 
13.1%
C 816
 
9.1%
P 407
 
4.6%
D 355
 
4.0%
Space Separator
ValueCountFrequency (%)
1619
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61915
96.8%
Common 2026
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7396
11.9%
e 7264
11.7%
d 5277
 
8.5%
t 5223
 
8.4%
o 5171
 
8.4%
u 4106
 
6.6%
r 3290
 
5.3%
l 3251
 
5.3%
h 3238
 
5.2%
c 3145
 
5.1%
Other values (11) 14554
23.5%
Common
ValueCountFrequency (%)
1619
79.9%
- 407
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7396
11.6%
e 7264
11.4%
d 5277
 
8.3%
t 5223
 
8.2%
o 5171
 
8.1%
u 4106
 
6.4%
r 3290
 
5.1%
l 3251
 
5.1%
h 3238
 
5.1%
c 3145
 
4.9%
Other values (13) 16580
25.9%

Marital_Status
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing579
Missing (%)7.1%
Memory size63.4 KiB
Married
3767 
Single
3144 
Divorced
611 

Length

Max length8
Median length7
Mean length6.6632545
Min length6

Characters and Unicode

Total characters50121
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 3767
46.5%
Single 3144
38.8%
Divorced 611
 
7.5%
(Missing) 579
 
7.1%

Length

2023-10-06T09:35:19.809432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:19.971169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
married 3767
50.1%
single 3144
41.8%
divorced 611
 
8.1%

Most occurring characters

ValueCountFrequency (%)
r 8145
16.3%
i 7522
15.0%
e 7522
15.0%
d 4378
8.7%
M 3767
7.5%
a 3767
7.5%
S 3144
 
6.3%
n 3144
 
6.3%
g 3144
 
6.3%
l 3144
 
6.3%
Other values (4) 2444
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42599
85.0%
Uppercase Letter 7522
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8145
19.1%
i 7522
17.7%
e 7522
17.7%
d 4378
10.3%
a 3767
8.8%
n 3144
 
7.4%
g 3144
 
7.4%
l 3144
 
7.4%
v 611
 
1.4%
o 611
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
M 3767
50.1%
S 3144
41.8%
D 611
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 50121
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 8145
16.3%
i 7522
15.0%
e 7522
15.0%
d 4378
8.7%
M 3767
7.5%
a 3767
7.5%
S 3144
 
6.3%
n 3144
 
6.3%
g 3144
 
6.3%
l 3144
 
6.3%
Other values (4) 2444
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 8145
16.3%
i 7522
15.0%
e 7522
15.0%
d 4378
8.7%
M 3767
7.5%
a 3767
7.5%
S 3144
 
6.3%
n 3144
 
6.3%
g 3144
 
6.3%
l 3144
 
6.3%
Other values (4) 2444
 
4.9%

Income_Category
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.1%
Missing889
Missing (%)11.0%
Memory size63.4 KiB
Less than $40K
2812 
$40K - $60K
1453 
$80K - $120K
1237 
$60K - $80K
1122 
$120K +
588 

Length

Max length14
Median length12
Mean length12.015114
Min length7

Characters and Unicode

Total characters86653
Distinct characters18
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLess than $40K
2nd rowLess than $40K
3rd row$40K - $60K
4th rowLess than $40K
5th row$40K - $60K

Common Values

ValueCountFrequency (%)
Less than $40K 2812
34.7%
$40K - $60K 1453
17.9%
$80K - $120K 1237
15.3%
$60K - $80K 1122
 
13.9%
$120K + 588
 
7.3%
(Missing) 889
 
11.0%

Length

2023-10-06T09:35:20.099972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:20.262169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4400
20.9%
40k 4265
20.3%
less 2812
13.4%
than 2812
13.4%
60k 2575
12.2%
80k 2359
11.2%
120k 1825
8.7%

Most occurring characters

ValueCountFrequency (%)
13836
16.0%
$ 11024
12.7%
K 11024
12.7%
0 11024
12.7%
s 5624
 
6.5%
4 4265
 
4.9%
- 3812
 
4.4%
e 2812
 
3.2%
L 2812
 
3.2%
n 2812
 
3.2%
Other values (8) 17608
20.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23873
27.6%
Lowercase Letter 19684
22.7%
Space Separator 13836
16.0%
Uppercase Letter 13836
16.0%
Currency Symbol 11024
12.7%
Dash Punctuation 3812
 
4.4%
Math Symbol 588
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11024
46.2%
4 4265
 
17.9%
6 2575
 
10.8%
8 2359
 
9.9%
1 1825
 
7.6%
2 1825
 
7.6%
Lowercase Letter
ValueCountFrequency (%)
s 5624
28.6%
e 2812
14.3%
n 2812
14.3%
a 2812
14.3%
h 2812
14.3%
t 2812
14.3%
Uppercase Letter
ValueCountFrequency (%)
K 11024
79.7%
L 2812
 
20.3%
Space Separator
ValueCountFrequency (%)
13836
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 11024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3812
100.0%
Math Symbol
ValueCountFrequency (%)
+ 588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53133
61.3%
Latin 33520
38.7%

Most frequent character per script

Common
ValueCountFrequency (%)
13836
26.0%
$ 11024
20.7%
0 11024
20.7%
4 4265
 
8.0%
- 3812
 
7.2%
6 2575
 
4.8%
8 2359
 
4.4%
1 1825
 
3.4%
2 1825
 
3.4%
+ 588
 
1.1%
Latin
ValueCountFrequency (%)
K 11024
32.9%
s 5624
16.8%
e 2812
 
8.4%
L 2812
 
8.4%
n 2812
 
8.4%
a 2812
 
8.4%
h 2812
 
8.4%
t 2812
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13836
16.0%
$ 11024
12.7%
K 11024
12.7%
0 11024
12.7%
s 5624
 
6.5%
4 4265
 
4.9%
- 3812
 
4.4%
e 2812
 
3.2%
L 2812
 
3.2%
n 2812
 
3.2%
Other values (8) 17608
20.3%

Card_Category
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Blue
7557 
Silver
 
436
Gold
 
93
Platinum
 
15

Length

Max length8
Median length4
Mean length4.1150475
Min length4

Characters and Unicode

Total characters33336
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowGold
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 7557
93.3%
Silver 436
 
5.4%
Gold 93
 
1.1%
Platinum 15
 
0.2%

Length

2023-10-06T09:35:20.422013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:20.570085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 7557
93.3%
silver 436
 
5.4%
gold 93
 
1.1%
platinum 15
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25235
75.7%
Uppercase Letter 8101
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 8101
32.1%
e 7993
31.7%
u 7572
30.0%
i 451
 
1.8%
v 436
 
1.7%
r 436
 
1.7%
o 93
 
0.4%
d 93
 
0.4%
a 15
 
0.1%
t 15
 
0.1%
Other values (2) 30
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 7557
93.3%
S 436
 
5.4%
G 93
 
1.1%
P 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 33336
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Months_on_book
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.92359
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:20.709039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0243588
Coefficient of variation (CV)0.22337297
Kurtosis0.35746635
Mean35.92359
Median Absolute Deviation (MAD)4
Skewness-0.10943003
Sum291017
Variance64.390334
MonotonicityNot monotonic
2023-10-06T09:35:20.847596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 1950
24.1%
39 276
 
3.4%
37 276
 
3.4%
38 274
 
3.4%
40 269
 
3.3%
34 267
 
3.3%
35 256
 
3.2%
31 255
 
3.1%
33 250
 
3.1%
41 243
 
3.0%
Other values (34) 3785
46.7%
ValueCountFrequency (%)
13 57
0.7%
14 13
 
0.2%
15 28
 
0.3%
16 20
 
0.2%
17 31
 
0.4%
18 46
0.6%
19 54
0.7%
20 63
0.8%
21 71
0.9%
22 83
1.0%
ValueCountFrequency (%)
56 78
1.0%
55 33
 
0.4%
54 43
 
0.5%
53 63
0.8%
52 52
 
0.6%
51 64
0.8%
50 87
1.1%
49 114
1.4%
48 133
1.6%
47 139
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8132329
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:20.954731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5518377
Coefficient of variation (CV)0.40696115
Kurtosis-0.99950613
Mean3.8132329
Median Absolute Deviation (MAD)1
Skewness-0.16312656
Sum30891
Variance2.4082002
MonotonicityNot monotonic
2023-10-06T09:35:21.065052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
2 985
12.2%
1 726
 
9.0%
ValueCountFrequency (%)
1 726
 
9.0%
2 985
12.2%
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
ValueCountFrequency (%)
6 1488
18.4%
5 1511
18.7%
4 1539
19.0%
3 1852
22.9%
2 985
12.2%
1 726
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3468708
Minimum0
Maximum6
Zeros22
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:21.176111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0141769
Coefficient of variation (CV)0.43214006
Kurtosis1.1291968
Mean2.3468708
Median Absolute Deviation (MAD)1
Skewness0.64425849
Sum19012
Variance1.0285547
MonotonicityNot monotonic
2023-10-06T09:35:21.277223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3094
38.2%
2 2611
32.2%
1 1780
22.0%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
0 22
 
0.3%
ValueCountFrequency (%)
0 22
 
0.3%
1 1780
22.0%
2 2611
32.2%
3 3094
38.2%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
ValueCountFrequency (%)
6 104
 
1.3%
5 144
 
1.8%
4 346
 
4.3%
3 3094
38.2%
2 2611
32.2%
1 1780
22.0%
0 22
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4503148
Minimum0
Maximum6
Zeros312
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:21.383824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1006873
Coefficient of variation (CV)0.44920241
Kurtosis0.029614465
Mean2.4503148
Median Absolute Deviation (MAD)1
Skewness0.020659003
Sum19850
Variance1.2115126
MonotonicityNot monotonic
2023-10-06T09:35:21.480800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 2716
33.5%
2 2596
32.0%
1 1207
14.9%
4 1092
13.5%
0 312
 
3.9%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
0 312
 
3.9%
1 1207
14.9%
2 2596
32.0%
3 2716
33.5%
4 1092
13.5%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
6 45
 
0.6%
5 133
 
1.6%
4 1092
13.5%
3 2716
33.5%
2 2596
32.0%
1 1207
14.9%
0 312
 
3.9%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION 

Distinct5325
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8636.5481
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:21.615638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.3
Q12555
median4549
Q311128
95-th percentile34058
Maximum34516
Range33077.7
Interquartile range (IQR)8573

Descriptive statistics

Standard deviation9086.4196
Coefficient of variation (CV)1.0520893
Kurtosis1.777607
Mean8636.5481
Median Absolute Deviation (MAD)2597
Skewness1.6576093
Sum69964676
Variance82563020
MonotonicityNot monotonic
2023-10-06T09:35:21.761086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 413
 
5.1%
34516 399
 
4.9%
9959 15
 
0.2%
15987 14
 
0.2%
23981 9
 
0.1%
6224 8
 
0.1%
2490 8
 
0.1%
3735 7
 
0.1%
7469 6
 
0.1%
1963 6
 
0.1%
Other values (5315) 7216
89.1%
ValueCountFrequency (%)
1438.3 413
5.1%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 1
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 1
 
< 0.1%
1452 2
 
< 0.1%
1454 1
 
< 0.1%
ValueCountFrequency (%)
34516 399
4.9%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34058 1
 
< 0.1%
33996 1
 
< 0.1%
33951 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1883
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1160.3828
Minimum0
Maximum2517
Zeros1986
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:21.923649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1326
median1273
Q31782
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1456

Descriptive statistics

Standard deviation815.50429
Coefficient of variation (CV)0.70278903
Kurtosis-1.1491884
Mean1160.3828
Median Absolute Deviation (MAD)590
Skewness-0.1444843
Sum9400261
Variance665047.25
MonotonicityNot monotonic
2023-10-06T09:35:22.085178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
2517 415
 
5.1%
1965 11
 
0.1%
1720 11
 
0.1%
1434 10
 
0.1%
1176 9
 
0.1%
1566 9
 
0.1%
1010 9
 
0.1%
1384 9
 
0.1%
1647 9
 
0.1%
Other values (1873) 5623
69.4%
ValueCountFrequency (%)
0 1986
24.5%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
193 2
 
< 0.1%
ValueCountFrequency (%)
2517 415
5.1%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 1
 
< 0.1%
2508 1
 
< 0.1%
2507 3
 
< 0.1%
2506 1
 
< 0.1%
2505 2
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

HIGH CORRELATION 

Distinct5757
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7476.1653
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:22.245484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile489
Q11341
median3495
Q39942
95-th percentile32099
Maximum34516
Range34513
Interquartile range (IQR)8601

Descriptive statistics

Standard deviation9080.2799
Coefficient of variation (CV)1.2145638
Kurtosis1.7726129
Mean7476.1653
Median Absolute Deviation (MAD)2674
Skewness1.6540051
Sum60564415
Variance82451483
MonotonicityNot monotonic
2023-10-06T09:35:22.407939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 261
 
3.2%
34516 81
 
1.0%
31999 21
 
0.3%
447 6
 
0.1%
787 6
 
0.1%
1129 6
 
0.1%
953 6
 
0.1%
990 6
 
0.1%
837 6
 
0.1%
933 6
 
0.1%
Other values (5747) 7696
95.0%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
42 1
< 0.1%
ValueCountFrequency (%)
34516 81
1.0%
34362 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34119 1
 
< 0.1%
34117 1
 
< 0.1%
34084 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1089
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76080879
Minimum0
Maximum2.675
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:22.573177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.466
Q10.632
median0.738
Q30.859
95-th percentile1.106
Maximum2.675
Range2.675
Interquartile range (IQR)0.227

Descriptive statistics

Standard deviation0.21666781
Coefficient of variation (CV)0.28478615
Kurtosis6.6213016
Mean0.76080879
Median Absolute Deviation (MAD)0.113
Skewness1.4913244
Sum6163.312
Variance0.046944938
MonotonicityNot monotonic
2023-10-06T09:35:22.725571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.767 28
 
0.3%
0.718 27
 
0.3%
0.76 27
 
0.3%
0.743 27
 
0.3%
0.725 26
 
0.3%
0.742 26
 
0.3%
0.69 26
 
0.3%
0.722 26
 
0.3%
0.791 26
 
0.3%
0.717 26
 
0.3%
Other values (1079) 7836
96.7%
ValueCountFrequency (%)
0 4
< 0.1%
0.01 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
0.166 1
 
< 0.1%
0.175 1
 
< 0.1%
ValueCountFrequency (%)
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.271 1
< 0.1%
2.204 1
< 0.1%
2.175 1
< 0.1%
2.145 1
< 0.1%
2.121 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

HIGH CORRELATION 

Distinct4462
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4402.9881
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:22.878768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1284
Q12160
median3897
Q34739
95-th percentile14215
Maximum18484
Range17974
Interquartile range (IQR)2579

Descriptive statistics

Standard deviation3401.7095
Coefficient of variation (CV)0.77259112
Kurtosis3.904927
Mean4402.9881
Median Absolute Deviation (MAD)1298
Skewness2.0479515
Sum35668607
Variance11571628
MonotonicityNot monotonic
2023-10-06T09:35:23.025482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4220 9
 
0.1%
4518 9
 
0.1%
4498 9
 
0.1%
4317 8
 
0.1%
4509 8
 
0.1%
4869 8
 
0.1%
4833 8
 
0.1%
4037 7
 
0.1%
1409 7
 
0.1%
4503 7
 
0.1%
Other values (4452) 8021
99.0%
ValueCountFrequency (%)
510 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
644 1
< 0.1%
647 2
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.907789
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:23.174142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.556379
Coefficient of variation (CV)0.36292068
Kurtosis-0.37133836
Mean64.907789
Median Absolute Deviation (MAD)17
Skewness0.15361702
Sum525818
Variance554.90298
MonotonicityNot monotonic
2023-10-06T09:35:23.328617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 168
 
2.1%
82 168
 
2.1%
81 164
 
2.0%
74 160
 
2.0%
75 160
 
2.0%
76 159
 
2.0%
69 159
 
2.0%
70 158
 
2.0%
73 157
 
1.9%
71 157
 
1.9%
Other values (116) 6491
80.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 3
 
< 0.1%
14 8
 
0.1%
15 15
0.2%
16 9
0.1%
17 12
0.1%
18 21
0.3%
19 10
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 4
 
< 0.1%
130 4
 
< 0.1%
129 5
0.1%
128 10
0.1%
127 11
0.1%
126 6
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct795
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71217615
Minimum0
Maximum3.714
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:23.485077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.367
Q10.583
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.23932079
Coefficient of variation (CV)0.33604157
Kurtosis16.555569
Mean0.71217615
Median Absolute Deviation (MAD)0.117
Skewness2.1268648
Sum5769.339
Variance0.057274443
MonotonicityNot monotonic
2023-10-06T09:35:23.656817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 141
 
1.7%
1 128
 
1.6%
0.75 128
 
1.6%
0.5 119
 
1.5%
0.6 95
 
1.2%
0.8 86
 
1.1%
0.714 77
 
1.0%
0.833 75
 
0.9%
0.778 57
 
0.7%
0.625 50
 
0.6%
Other values (785) 7145
88.2%
ValueCountFrequency (%)
0 6
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 1
 
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%
2.4 2
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct943
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27318664
Minimum0
Maximum0.999
Zeros1986
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-10-06T09:35:23.852658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.022
median0.174
Q30.497
95-th percentile0.789
Maximum0.999
Range0.999
Interquartile range (IQR)0.475

Descriptive statistics

Standard deviation0.27459484
Coefficient of variation (CV)1.0051547
Kurtosis-0.77916141
Mean0.27318664
Median Absolute Deviation (MAD)0.174
Skewness0.72609436
Sum2213.085
Variance0.075402324
MonotonicityNot monotonic
2023-10-06T09:35:24.001815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
0.073 35
 
0.4%
0.057 26
 
0.3%
0.07 25
 
0.3%
0.048 25
 
0.3%
0.053 24
 
0.3%
0.06 24
 
0.3%
0.069 24
 
0.3%
0.061 23
 
0.3%
0.071 22
 
0.3%
Other values (933) 5887
72.7%
ValueCountFrequency (%)
0 1986
24.5%
0.004 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 2
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.99 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%
0.978 1
 
< 0.1%
0.977 1
 
< 0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
1
6801 
0
1300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Length

2023-10-06T09:35:24.135017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-06T09:35:24.259054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Interactions

2023-10-06T09:35:15.260247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.216537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:45.340906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.286692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.386684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.306864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.287934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.268673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.290055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.306550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.374704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.311122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.335138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.316706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.343617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.418060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.389635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.352051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:45.489208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.431701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.515188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.441966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.415182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.402986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.422101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.440394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.505001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.442033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.468665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.465144image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.476531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.571683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.510182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.479542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:45.616707image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.577804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.637281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.557939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.538935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.531223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.552718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.574859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.624891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.570286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.590309image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.594651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.600101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.725592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.630576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.600727image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:46.381046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.715899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.751159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.677538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.654781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.651020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.679734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.692819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.738703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.684129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.701525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.712708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.714605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.864547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.749993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.729841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:46.504668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.846759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.866721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.797871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.773141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.766582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.800572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.815644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.858047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.807481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.820802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.835598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.835670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.997864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.855153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.848645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:46.620116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.971120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.971494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.907563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.881681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.888496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.924760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.930060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.966949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.918226image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.927722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.947438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.942008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.126517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:15.974625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:43.974911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:46.747899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.105744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.085140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.032523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.995269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.011236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.048586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.051601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.085323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.045588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.050010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.072505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.068053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.266992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.100061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.102004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:46.885390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.234979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.204159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.162001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.121155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.131929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.170065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.176693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.205429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.181025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.166928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.195614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.193449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.410556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.219579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.235545image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.078992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.365171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.325297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.316550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.239059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.260056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.285257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.300172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.332532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.314548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.289182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.320660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.359031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.545518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.345336image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.381764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.253475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.491898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.452465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.448599image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.369680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.388709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.410817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.426958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.459111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.442500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.412658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.446537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.506571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.679947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.475432image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.520112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.387377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.605237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.572232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.561868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.487736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.510424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.535250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.549631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.577652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.569102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.530993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.570247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.628968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.799733image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.621694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.658757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.570602image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.730668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.695027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.683367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.625740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.645189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.666714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.677958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.700850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.695517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.673157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.704492image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.763148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:13.944883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.745243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.785708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.708769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.844296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.813482image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.796709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.749313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.764429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.792142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.815114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.820981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.821149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.795448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.830960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:11.888001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:14.074762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:16.882391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:44.929938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:47.861109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:49.975867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:51.942015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:53.927688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:55.883047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:57.903277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:59.922791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:01.958542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:03.947696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:05.952827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:07.919994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:09.956682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.023209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:14.205126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:17.016611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:45.069167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.011449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.110872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.059004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.049656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.005794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.027278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.052093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.091275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.069721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.081194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.045115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.089087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.153975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:14.330572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:17.161054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:45.209123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:48.154690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:50.249547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:52.184592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:54.172404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:56.142356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:34:58.159927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:00.186960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:02.240501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:04.193037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:06.213499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:08.186857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:10.221091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:12.288817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-06T09:35:14.455103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-06T09:35:24.381657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
train_idxCLIENTNUMCustomer_AgeDependent_countMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioGenderEducation_LevelMarital_StatusIncome_CategoryCard_CategoryAttrition_Flag
train_idx1.0000.0020.002-0.0040.015-0.0090.0030.001-0.006-0.005-0.0030.0050.0140.0180.0090.0020.0070.0020.0000.0000.0000.000
CLIENTNUM0.0021.0000.017-0.0200.1110.017-0.0090.0100.015-0.0010.0140.028-0.0020.0050.0090.0030.0000.0170.0000.0000.0000.045
Customer_Age0.0020.0171.000-0.1390.773-0.0120.039-0.000-0.0030.011-0.006-0.077-0.036-0.057-0.0320.0100.0000.0260.0930.0810.0240.037
Dependent_count-0.004-0.020-0.1391.000-0.115-0.0310.001-0.0480.0560.0030.056-0.0280.0510.0430.001-0.0320.0090.0050.0360.0510.0130.014
Months_on_book0.0150.1110.773-0.1151.000-0.0110.0580.0030.0030.0100.004-0.063-0.030-0.045-0.032-0.0020.0000.0000.0510.0540.0030.015
Total_Relationship_Count-0.0090.017-0.012-0.031-0.0111.000-0.0150.060-0.0590.011-0.0710.028-0.271-0.2200.0190.0650.0200.0000.0110.0180.0680.164
Months_Inactive_12_mon0.003-0.0090.0390.0010.058-0.0151.0000.031-0.033-0.041-0.022-0.026-0.028-0.046-0.051-0.0210.0200.0000.0060.0180.0000.203
Contacts_Count_12_mon0.0010.010-0.000-0.0480.0030.0600.0311.0000.024-0.0500.038-0.019-0.176-0.177-0.096-0.0680.0630.0000.0000.0220.0000.246
Credit_Limit-0.0060.015-0.0030.0560.003-0.059-0.0330.0241.0000.1420.9320.0150.0240.033-0.016-0.4110.4430.0000.0280.3250.3350.038
Total_Revolving_Bal-0.005-0.0010.0110.0030.0100.011-0.041-0.0500.1421.000-0.1430.0330.0240.0500.0820.7050.0330.0200.0220.0210.0230.403
Avg_Open_To_Buy-0.0030.014-0.0060.0560.004-0.071-0.0220.0380.932-0.1431.0000.0020.0150.017-0.045-0.6810.4450.0000.0330.3250.3360.024
Total_Amt_Chng_Q4_Q10.0050.028-0.077-0.028-0.0630.028-0.026-0.0190.0150.0330.0021.0000.1280.0770.2910.0330.0650.0180.0700.0350.0120.229
Total_Trans_Amt0.014-0.002-0.0360.051-0.030-0.271-0.028-0.1760.0240.0240.0150.1281.0000.8800.2280.0300.2500.0000.1290.1050.1590.325
Total_Trans_Ct0.0180.005-0.0570.043-0.045-0.220-0.046-0.1770.0330.0500.0170.0770.8801.0000.2350.0500.1660.0130.1250.0660.1100.464
Total_Ct_Chng_Q4_Q10.0090.009-0.0320.001-0.0320.019-0.051-0.096-0.0160.082-0.0450.2910.2280.2351.0000.0980.0480.0080.0430.0320.0000.317
Avg_Utilization_Ratio0.0020.0030.010-0.032-0.0020.065-0.021-0.068-0.4110.705-0.6810.0330.0300.0500.0981.0000.2790.0000.0340.1910.1450.242
Gender0.0070.0000.0000.0090.0000.0200.0200.0630.4430.0330.4450.0650.2500.1660.0480.2791.0000.0220.0000.8310.0810.046
Education_Level0.0020.0170.0260.0050.0000.0000.0000.0000.0000.0200.0000.0180.0000.0130.0080.0000.0221.0000.0190.0090.0230.042
Marital_Status0.0000.0000.0930.0360.0510.0110.0060.0000.0280.0220.0330.0700.1290.1250.0430.0340.0000.0191.0000.0000.0300.009
Income_Category0.0000.0000.0810.0510.0540.0180.0180.0220.3250.0210.3250.0350.1050.0660.0320.1910.8310.0090.0001.0000.0550.027
Card_Category0.0000.0000.0240.0130.0030.0680.0000.0000.3350.0230.3360.0120.1590.1100.0000.1450.0810.0230.0300.0551.0000.016
Attrition_Flag0.0000.0450.0370.0140.0150.1640.2030.2460.0380.4030.0240.2290.3250.4640.3170.2420.0460.0420.0090.0270.0161.000

Missing values

2023-10-06T09:35:17.364248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-06T09:35:17.743625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-06T09:35:17.990739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
0071307138354F1NaNSingleNaNBlue361333723.017281995.00.5958554990.6780.4641
1171424633358F4High SchoolMarriedNaNBlue481435396.018033593.00.4932107390.3930.3340
2271820678345F4NaNSingleLess than $40KGold3661315987.0164814339.00.7321436361.2500.1031
3372109698334F2GraduateSingleLess than $40KBlue364343625.025171108.01.1582616461.3000.6941
4472002868349F2High SchoolMarried$40K - $60KBlue395342720.01926794.00.6023806610.7940.7081
5577894223360F0DoctorateMarriedLess than $40KBlue455241438.3648790.30.4771267271.0770.4511
6670868290843F4NaNSingleNaNBlue282212838.01934904.00.8738644870.5540.6811
7772067045852F2NaNSingle$40K - $60KBlue453133476.015601916.00.8943496580.8710.4491
8871995240830M0GraduateMarriedLess than $40KBlue363322550.01623927.00.6501870510.2750.6361
9970841275833F3GraduateSingleLess than $40KBlue365231457.001457.00.6772200450.3640.0000
train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
8091809171482540845F3High SchoolSingle$40K - $60KBlue362332853.02517336.00.5954971650.7570.8821
8092809281246595853F3Post-GraduateDivorcedNaNBlue4852212286.099711289.00.7264960830.7290.0811
8093809370927458340M2High SchoolMarried$120K +Blue2753112248.0132310925.00.8824806910.8960.1081
8094809470821775863M2GraduateMarried$60K - $80KBlue4952314035.0206111974.02.2711606301.5000.1471
8095809571814835850F3High SchoolMarriedLess than $40KBlue362331572.001572.00.7402447410.5770.0000
8096809676905303344F1GraduateSingle$40K - $60KBlue383254142.025171625.00.8092104440.8330.6080
8097809771440615853F3High SchoolDivorcedNaNBlue364367939.007939.00.5512269420.3120.0000
8098809871414013342F4GraduateNaNLess than $40KBlue323122314.01547767.00.8044678741.0000.6691
8099809972024498340M3NaNSingle$40K - $60KBlue284113563.017071856.00.5061482420.3120.4791
8100810082712388353M4High SchoolSingle$60K - $80KBlue495123858.003858.00.6704472920.6140.0001